Method and apparatus to improve the convergence speed of a recursive motion estimator

ABSTRACT

A method and apparatus for motion estimation of at least a first and a second image frame by estimating at least one motion vector correlating a portion of pixels of the at least first and second image frame, the first and second image frame being part of an image frame sequence. The at least one motion vector is obtained by a predominant motion detection generating at least one global motion vector based on at least one previously determined motion vector, the previously determined motion vector correlating a portion of pixels of earlier image frames of the image frame sequence, and an estimation estimating the at least one motion vector based on the at least one global motion vector.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to a method and an apparatus for pictureimprovement and in particularly in the field of video coding andcompression and/or motion compensated frame rate conversion. Moreover,the present invention relates to a method and an apparatus for motionvector field post processing to improve the convergence speed of arecursive motion estimator.

A motion estimator provides motion vectors and/or, after an analysis, amotion model, wherein the motion model basically reflects camera motionssuch as dolly (forward, backwards), track (left, right), boom (up,down), pan (left, right), tilt (up, down) and/or roll (along the viewaxis). There are different ways of motion estimation with variousapplications: it may be used for coding or for motion compensated framerate conversion or for global motion compensation.

The detection of global motion in a recursive motion estimator is aknown approach. Example vectors are taken out of an estimated motionvector field and are used to solve a set of equations, which define aparametric motion model. The vector field is sampled at for example 9equally distributed locations. These 9 motion vectors are taken to solvea 4-parameter equation system. This motion model provides one singleglobal motion vector, which can be used during the motion vectorprocess; but only if the parameters of the motion model are determinedwell according to quality criteria. Since the quality criteria are verystrict and decide if the global motion vector is good enough, the globalmotion vector is not used often enough.

The reason for this is that the samples in the motion vector field areon fixed positions, which cover not necessarily the points, where globalmotion happens. They can e.g. sample vectors, which relate to localmotion.

The problem to be solved is to improve the convergence speed of arecursive motion estimator. In case of very fast and sudden globalmotion scenes, the motion estimator cannot catch the motion over thecomplete picture scene. This results in visible picture artifacts due toa failed motion compensation process.

SUMMARY

It is an objective of the present invention to provide a method and anapparatus operable to support a recursive motion estimation process.

Another object is to improve the convergence speed of a motionestimator.

Another object is to improve the motion estimation method for motioncompensated frame rate conversion.

Another objective of the invention is to improve image quality of videosignals.

These objectives are solved by a method for motion estimation of atleast a first and a second image frame by estimating at least one motionvector correlating a portion of pixels of said at least first and secondimage frame, said first and second image frame being part of an imageframe sequence, wherein said at least one motion vector is obtained by:

-   -   a predominant motion detection step for generating at least one        global motion vector based on at least one previously determined        motion vector, said previously determined motion vector        correlating a portion of pixels of earlier image frames of said        image frame sequence, and    -   an estimation step for estimating said at least one motion        vector on the basis of said at least one global motion vector.

Favourably, said predominant motion detection step comprises a histogramstep for forming a histogram on the basis of said at least one motionvector, a filtering step for filtering said at least one motion vectoron the basis of said histogram, and a distribution step for distributingthe at least one filtered motion vector over at least a portion of saidat least second image frame.

Favourably, the histogram step, the filtering step and the distributionstep of said predominant motion detection step are performed in relationto at least one characteristic of said at least one motion vector, saidcharacteristics being represented by said histogram.

Favourably, said motion vector is describing the change of pixelcharacteristics within two frames.

Favourably, said distribution step is using a predominant predictorfield describing a global motion of said at least a portion of saidimage frame.

Favourably, said histogram step comprises a histogram calculation stepfor processing said at least one motion vector into a histogram and ahistogram analysis step for extracting characteristics out of saidhistogram.

Favourably, said filtering step comprises a sorting step for sortingsaid motion vectors,

a binarization step for selecting none or at least one of said sortedmotion vectors, and

a substitution step for substituting specifically said none or at leastone selected motion vector by none or at least one non-selected motionvector.

Favourably, said binarization step is selecting by means of a filteringparameter.

Favourably, said distribution step distributes said filtered motionvectors spatial equally.

Favourably, said method comprises a segmentation step for detecting atleast one segment of said image frame, wherein said at least one segmentis characterised by a specific distribution of motion vectors and/orpixel characteristics.

Favourably, said method comprises a motion model classification step fordetecting motion models of motion vectors.

Favourably, said motion models comprise tilting, panning, zooming,rotations, chaotic and/or complex motions.

Favourably, the distribution step distributes motion vectors dependenton the maximum occurrences of said motion vector or on the segment or onthe calculation per position.

Favourably, a method for processing video signals into motion vectorfields comprises a motion estimation step for receiving and processinginput video signals and predominant predictor fields, and outputtingmotion vector fields, and a predominant motion detection step forapplying above-mentioned method, wherein said method is operable toreceive and process said motion vector fields, and output saidpredominant predictor fields.

Favourably, a method for processing video signals comprises a videoprocessing step for receiving and processing input video signals andmotion vector fields, and outputting video signals, and above-mentionedmethod for receiving and processing input video signals, and outputtingmotion vector fields.

Furthermore the above-mentioned objectives are also solved by anapparatus adapted for motion estimation of at least a first and a secondimage frame, said apparatus estimating at least one motion vectorcorrelating a portion of pixels of said at least first and second imageframe, said first and second image frame being part of an image framesequence, wherein said apparatus comprises:

-   -   a predominant motion detector operable to generate at least one        global motion vector based on at least one previously determined        motion vector, said previously determined motion vector        correlating a portion of pixels of earlier image frames of said        image frame sequence, and    -   a motion estimator system operable to estimate said at least one        motion vector on the basis of said at least one global motion        vector.

Favourably, said predominant motion detector comprises a histogramdevice operable to form a histogram on the basis of said at least onemotion vector, a filtering device operable to filter said at least onemotion vector on the basis of said histogram, and a distribution deviceoperable to distribute the at least one filtered motion vector over atleast a portion of said at least second image frame.

Favourably, the histogram device, the filtering device and thedistribution device of said predominant motion detector are operable toperform in relation to at least one characteristic of said at least onemotion vector, said characteristics being represented by said histogram.

Favourably, said motion vector is describing the change of pixelcharacteristics within two frames.

Favourably, said distribution device is operable to use a predominantpredictor field describing a global motion of said at least a portion ofsaid image frame.

Favourably, said histogram device comprises a histogram calculationdevice operable to process said at least one motion vector into ahistogram and a histogram analysis device operable to extractcharacteristics out of said histogram.

Favourably, said filtering device comprises a sorting device operable tosort said motion vectors, a binarization device operable to select noneor at least one of said sorted motion vectors, and a substitution deviceoperable to substitute specifically said none or at least one selectedmotion vector by at least one of said non-selected motion vector.

Favourably, said binarization device is operable to select by means of afiltering parameter.

Favourably, said distribution device is operable to distribute saidfiltered motion vectors spatial equally.

Favourably, said apparatus comprises a segmentation device operable todetect at least one segment of said image frame, wherein said at leastone segment is characterised by a specific distribution of motionvectors and/or pixel characteristics.

Favourably, said apparatus comprises a motion model classificationdevice operable to detect motion models of motion vectors.

Favourably, said motion models comprise tilting, panning, zooming,rotations, chaotic and/or complex motions.

Favourably, said distribution device is operable to distribute motionvectors dependent on the maximum occurrences of said motion vector or onthe segment or on the calculation per position.

Favourably, an apparatus operable to process video signals into motionvector fields comprises a motion estimation device operable to receiveand process input video signals and predominant predictor fields, andoutput motion vector fields, and a predominant motion detection devicecomprising an above-mentioned apparatus, wherein said apparatus isoperable to receive and process said motion vector fields, and outputsaid predominant predictor fields.

Favourably, a system operable to process video signals comprises a videoprocessing device operable to receive and process input video signalsand motion vector fields, and output video signals, and anabove-mentioned apparatus, wherein said apparatus is operable to receiveand process input video signals, and output motion vector fields.

It is already known that the determination of the global motion, whichmay come from a camera move, and the usage of a global motion vector asa candidate predictor in the motion estimation process can improve theresulting motion vector field. The basic idea of this invention is touse not only one single motion vector type to describe the global motionbut to determine with an reliable and robust process a certain number ofpredominant vector types which are used as predictors for the motionestimation process. Further it is important how these predominant motionvectors are actually used in the motion estimation process. A goodsolution has been achieved by generating a predictor field, whichcontains the different predominant motion vectors in a spatial equallydistributed order.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will become more apparent from the following detaileddescription when taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 shows an example of a schematic block diagram of a motioncompensated frame rate conversion system comprising an embodiment of thepresent invention,

FIG. 2 shows a schematic block diagram of an embodiment of the presentinvention,

FIG. 3 shows a schematic block diagram an alternative embodiment of thepresent invention,

FIG. 4 shows data samples of a filtering element, said element being aportion of an embodiment of the present invention,

FIG. 5 shows an example of distributed predominant motion vectors overan image, is shown.

DETAILED DESCRIPTION OF EMBODIMENT

FIG. 1 shows an example of a schematic block diagram of a motioncompensated frame rate conversion system 1 comprising an embodiment ofthe present invention, said system 1 is operable to receive and processinput video signal 7 and output video signal 8. The input video signal 7comprises data which may be uncompressed or already compressed, whilethe output video signal 8 comprises (further/additionally) compressedand/or converted data. Said system 1 comprises a predominant motiondetector 2, a motion estimator system 3 and a video processing system 4.

The input video signal 7 comprises data of at least a group of picturesand/or motion vectors, whereby said group of pictures comprises at leasta first and a second image frame and might be part of a image framesequence.

The motion estimator system 3 is operable to receive data comprising theinput video signal 7 and the predominant predictor field 6 sent by thepredominant motion detector 2, process said data 6 & 7 and output amotion vector field 5 to the video processing system 4 and/or thepredominant motion detector 2. Said motion vector field 5 comprisesmotion vectors describing the change of all pixels of two successiveimage frames, whereby the motion vector might point either to thepreceding or to the succeeding image frame. As known to a skilled personthe motion vector field 5 might only comprise at least one vectordescribing the displacement of at least one pixel of two successiveimage frames; or at least of frames that are temporally apart. Moreover,a motion vector can also describe the displacement of objects of animage frame, said object might be a segment(s) having the same and/orspecific pixel characteristics and/or other properties known to askilled person. For example a car, a person, a tree and other itemsknown to a skilled person are objects, whose movement caused by theobjects movement and/or the movement of a camera can be described bysaid motion vector.

These motion vectors as well as the later described global motionvectors allow to render a prediction of the movement of a pixel betweentwo image frames. There are temporal and spatial predictions, wherebythe temporal predictions are estimation results from previousestimations and the spatial predictions are estimation results from thecurrent estimations, said estimation being generated by said motionestimator system. The global motion vectors are additionally createdpredictors, which might base on the previous and/or the currentestimation. A predictor is recursively optimized vector which waspreviously found and is based on previously determined motion vectors.The 3D recursive search uses spatial and temporal predictions togenerate motion vector in a list, whereby the motion vector fitting bestto the actual motion vector correlating a first and a second image isacquired.

The motion vector of the motion vector field 5 is calculated based ontemporal, spatial and/or global motion predictions, respectively. Theinitial motion vectors required to determine said predictions mightnevertheless be calculated by the motion estimator system 3 in e.g. ablock matching algorithm. Since the subject-matter is a recursivemethod, the 3D recursive search is used for the block matching. For theblock matching algorithm the SAD (summed absolute difference) or the MSE(mean squared error) criterion is used to find the motion vectordescribing the best match of the pixel block.

In detail the motion estimator system is operable to estimate at leastone motion vector on the basis of at least one global motion vector.

The video processing system 4 is operable to receive data comprising theinput video signal 7 and the motion vector field 5 from the motionestimator system 3, process said data 5 & 7 and output the output videosignal 8. As known to a skilled person some image frames are substitutedby said motion vector field 5 to estimate the following or previousframe and thus compress the video data of a movie. The motioncompensated frame rate conversion system 1 can be applied to e.g. frameconversion, encoding and decoding, respectively.

The predominant motion detector 2 is operable to receive data comprisingthe motion vector field 5 sent by the motion estimator system 3, processsaid data 5 and output the predominant predictor field 6 to the motionestimator system 3. FIG. 2 describes, that the received data alsocomprises the input video signal 7 which is not explicitly shown in FIG.1.

In more detail the predominant motion detector is operable to generateat least one global motion vector based on said at least one previouslydetermined motion vector, said previously determined motion vectorcorrelating a portion of pixels of earlier image frames of said imageframe sequence. Earlier image frames might e.g. comprise a first and apreceding image frame. It has to be noted that the second image frame issucceeding to the first image frame, meaning after the first imageframe.

The predominant predictor field 6 describes the global motion of thepixels in the frames and comprises at least one predominant motionvector, whereby said predominant motion vectors are based on the vectorsof at least a specific portion of the motion vector field 5. Thispredominant predictor field 6 is operable to provide at least onepredominant motion vector for a respective pixel or a block of pixelswithin an image frame, whereby said predominant motion vector isoperable as a predictor as explained above. The better and more accuratethe prediction, the less motion vectors will be needed to be calculatedfor creating the next image frame and the faster the motion estimationconverges to the actual motion vector. Said global motion comprises anddescribes e.g. translations, rotations, zooming and/or other motionsknown by a skilled person. Furthermore the predominant motion vectorsare derived from the motion vector field 5 from a previous processingiteration of the motion estimator system 3 in order to save resourcecomplexity. Nevertheless, other origins of the motion vector field 5 arepossible like e.g. said motion vector field 5 being based and/or aportion of said input video signal 7.

The detector 2 can be seen as an extension to the motion estimatorsystem 3 in order to improve the output picture quality and reduce thebit rate in video coding by faster converging motion estimation. Byproviding the global motion vectors, also called predominant motionvectors, global motion predictions can be made in the detector 2, whichhelp to faster converge the motion estimation. The main advantageousdifferences between the invention and the state of the art are:

-   -   Visible improvement of the picture quality when applying this        feature to the motion estimator.    -   Not only one motion vector for description of the global motion        is used. This improves also the estimation on local motion of        large objects.    -   The system 1, in particular said detector 2, is easy to        implement in hardware and/or software; thus the embodiment of        the present invention is feasible as an apparatus and/or a        method.

Eventually the motion vector field 5 comprises motion vectors based onthe signal input 7 and/or the predominant predictor field 6. To providetrue-motion estimation, the true motion vector has to be calculated.Instead of actually calculating the true motion vector by e.g. a blockmatching algorithm, a prediction of said vector can be performed whichrequires less computing power.

This prediction is done by means of the predominant motion vectors ofthe predominant predictor field 6, which provide additional predictionfor the movement of the respective pixels according to the actual/truemotion vector. Of course since the motion estimator system 3 alsocalculates the spatial and/or the temporal predictors, the overallprediction of the true motion vector is more accurate and converges muchfaster.

The combination of predominant motion detector and the motion estimatorsystem, as shown in FIG. 1, is adapted for motion estimation of at leasta first and a second image frame by estimating at least one motionvector correlating a portion of pixels of said at least first and secondimage frame, said first and second image frame being part of an imageframe sequence.

Essentially, the predominant motion detector is operable to calculateglobal motion vectors provided in predominant predictor fields, wherebysaid global motion vectors base on motion vectors of previous imageframes like e.g. of the first frame and a preceding frame of said firstframe. The motion estimator system is operable to calculate the motionvector based on said global motion vectors on the basis of said motionvectors of said previous image frames.

FIG. 2 shows an embodiment of the present invention comprising apredominant motion detector 9, said detector 9 comprising a histogramelement 10, a filtering element 11, a segmentation element 12, a motionmodel classification element 13 and a distribution element 14. Thepredominant motion detector 9 is the same as the one described in FIG.1.

The histogram element 10 is operable to calculate and output data 15 ofa histogram comprising its characteristics and motion vectors, said databeing based on the received motion vector field 5. This motion vectorfield 5 is the same as described in FIG. 1.

The filtering element 11 is operable to receive and process said data15, and output data of predominant motion vector field 16. An example ofan filtering element 11 is described later in more detail in FIG. 3. Theprocessing in element 11 comprises elements for filtering, cleaningand/or additional processing elements known to a skilled person forprocessing of the histogram 15 to excerpt said data of predominantmotion vector field 16. The predominant motion vector field 16 bases onsaid data 15. In case of a linear translation one predominant motionvector can be calculated, but considering e.g. rotation motions therewill be a different specific distribution of different predominantmotion vectors. For example when a object is rotating with its rotationaxis pointed towards the camera, there will be an equal distribution ofdifferent predominant motion vectors for the area of this object. For arotation there are global motion vectors which are tangential alignedlike on the edge of a circle, whereby the axis of the center of thecircle corresponds to the rotation axis of the object or the camera. Theglobal motion vectors are all aligned to form a clockwise orcounterclockwise rotation. While the global motion vectors nearer to theaxis are shorter, the global motion vectors further away will be longer.

The segmentation element 12 is operable to identify segments in a videoframe of the video signal and output data 17 of segment representatives,said segments having characteristics like for example comprising thesame and/or similar motion vectors and/or similar pixel luminescenceand/or other characteristics known to a skilled person. Also specificdistribution(s) of predominant motion vectors are characteristics ofsegments like e.g. in a rotation. Therefore the incoming data comprisesthe predominant motion vector field 16 and, if necessary for comparisonof pixel characteristics, the input video signal 7. Said pixelcharacteristics describes the pattern, lumineszenz, colour and/orcontrast e.g. of said pixel and/or among pixels of the same or differentframes.

The motion model classification element 13 is operable to identifyspecific global motions like for example zooming, rotations, panning andother motions known to a skilled person. A motion model can beidentified by the distribution of predominant motion vectors 16 alsocalled global motion vectors and thus depends on the distribution of themotion vectors within the histogram and the position of the motionvector within the frame. According to this analysis, specific data 18 issent to the generate predictor element 14 to support the selectionprocess of the predictors, said data 18 comprises a motion modelidentification. The segmentation element 12 is provided with data 19 ofmotion segment from said element 13, said data 19 predicting a segmentwhich was identified due to its unique global motion like e.g.rotations.

The distribution element 14 is operable to generate and outputpredominant predictor fields 6. The generation of the fields 6 bases onone or several criteria like the one described in FIG. 3, wherein themaximum number of occurrences of a motion vector is key valueresponsible for selecting said predominant motion vectors. Moreover, thegeneration is alternatively based on other criteria like for example onthe segment representatives via data 17 or of the calculated predictorsper position.

Another criteria bases on the data 18 comprising the classified type ofmotion, whereby the predominant predictor vectors 6 per estimationposition can now be generated and/or the distribution pattern of thepredominant predictor vectors 6 can be reconsidered.

FIG. 3 shows an alternative embodiment of the present inventioncomprising a predominant motion detector 38, said detector 38 beingoperable to calculate predominant predictor field 6 based on motionvector field 5. The detector 38 is the same as the predominant motiondetector 2 described in FIG. 1 and comprises the histogram element 10,filtering element 11, distribution element 14 and switch off element 36.The histogram element 10 comprises a histogram calculation element 20and a histogram analysis element 21. The filtering element 11 comprisesa sorting element 25, a binarization element 26 and a substitutionelement 27. All elements mentioned both in the description of FIGS. 2and 3 are the same. All parameters mentioned in all Figures are presetand/or flexibly changeable.

The histogram calculation element 20 is operable to receive a motionvector field 5, and generate a two-dimensional histogram 22 of saidmotion vector field 5 by counting the number of occurrences for eachmotion vector mv=(mv_(x), mv_(y)), respectively. The parameter of saidmotion vectors mv_(x) and mv_(y) describe the respective componentlength of the vector in the X-Axis and Y-Axis. The motion vector field 5is the same as the one described in FIG. 1 or 2. It is possible tochoose here other types of histograms, which could be configured by ahistogram calculation parameter 23. Other alternative examples arepossible using separate histograms for mv_(x) and mv_(y) and/or evenhistograms of more than two dimensions, wherein for example spatialand/or temporal dependencies of a vector or of the ones in theneighborhood are also considered.

The histogram analysis element 21 is operable to receive thetwo-dimensional histogram 22 and to find a specified number of maxima inthe histogram 22, wherein the maxima selection is determined by ahistogram analysis parameter 24. Other parameters 24 being operable notonly for maxima selection are possible. The output of this element 21 isan unsorted list 30 comprising parameter 28 and 29, said parameter 28being the actual number of occurrences of the respective motion vectorand said parameter 29 being the motion vector itself or a valueidentifying the vector. The list 30 might comprise other parameters inreference to the dimensions and/or parameters of said histogram. Themotion vector mv=(0,0) is not considered because this is anyway adefault candidate in each motion estimation system and is therefore notof interest in this element.

The filtering element 11 is operable to apply a histogram postprocessing to the resulting data of said unsorted list 30, in particularto sort and filter out motion vectors, which are not considered aspredominant because their number of occurrences is lower than afiltering parameter 31, which is a preset or flexibly settable thresholdvalue. Other parameters 31 being operable for filtering according toother characteristics other than the number of occurrences are possible.If no motion vector 29 has a higher number of occurrences 28 than saidparameter 31, the list 30 is taken as it is because it may be forexample a zoom or rotation. The output of the element 11 is a list 35 ofpredictors, which contains the predominant motion vectors beginning withthe most dominant one and which is eventually substituted, indexed andsorted.

Essentially linear and/or non-linear filtering can be applied by thefiltering element 11. By filtering, faulty calculated predictors andthus motion vectors can be filtered out, thus maintaining the systemstabile. The segmentation of the histogram is an example of non-linearfiltering.

The sorting element 25 is operable to sort the list 30 by parameter 28and output the sorted list 32. Other parameters than the number ofoccurrences 28 output by element 10 can be considered for sorting saidlist 30. As later shown in FIG. 4 as an example, the motion vectorhaving the highest number of occurrences is at the top of the list,while the other motion vectors are listed in a descending order.

The binarization element 26 is operable to apply the parameter 31 tothis sorted list 32, which leads to a binarization of said list 32, inparticular of the predominant motion vectors 29 according to theirnumber of occurrences 28 and said parameter 31. Binarization means thatan additional index for every predominant motion vector 29 isimplemented into list 32, thus said list 32 being processed to anindexed & sorted list 33. By specifically indexing a predominant motionvector 29, said vector 29 is then selected for substitution processlater described. If the parameter 28 is greater or equal to parameter31, the comparison index of the specific predominant motion vector 29 isset to 1, otherwise it is set to 0.

The substitution element 27 is operable to substitute data sets of list33 comprising the parameter 28 and 29 and their indices with data setsfrom the top of the list 33, when the index is equal to 0. Generallysaid every predominant motion vector 29 which was selected in thebinarization element 26 is substituted by a non-selected and/orpredetermined motion vector. If no predominant motion vector 29 isselected, then no predominant motion vector may be substituted or all ofthem can be substituted. In this case the data from the top of the listcomprise the most dominant motion vectors and substitute the lessdominant motion vectors from the bottom of the list, but the scope ofthe invention is not limited to this example. The list 33 is processedto list 35 in element 27 by finally indexing the motion vectors 29 withascending numbers. The data samples shown in and described by FIG. 4depict the processing elements 25 to 27 in more detail.

The distribution element 14 is operable to apply the predominant motionvectors of the list 35 to the motion estimation process in a way thateach vector in the list 35 is spatial equally distributed over the wholepicture. FIG. 5 shows an example of a pattern how the predominant motionvectors can be distributed into a predominant predictor field 6.

The switch off element 36 is similar to a switch and operable to outputeither the previously distributed and predominant motion vectors as saidpredominant predictor field 6 or data containing only the zero vector tothe motion estimator system 3 described in FIG. 1. In case the system 3does not have the capability to process the predominant motion vectors,the user does not want to apply the global motion estimation or anyother possible situation known to a skilled person, the predominantmotion detector 38 is turned or cut off by means of said element 36. Aswitch off parameter 37 is operable to control said element 36.

FIG. 4 shows examples of three different tables 30, 33 and 35, alltables comprising at least two columns of data samples. One columncomprises the parameter 28 identifying the actual number of occurrencesof the respective motion vector and the other column comprises theparameter 29 identifying the motion vector itself. The motion vector 29mv[i] is always mapped to its respective number of occurrences 29num[i], like for example mv[0] is located in the same row as num[0]. Inthis example the parameter num[i] has the highest number, when the indexis the lowest; but the scope of the invention is not limited to thisexample. The tables 30 and 35 are the same as the data 15 and 16,respectively, described in FIG. 3.

Table 30 is an unsorted list, which is sent to element 11, in particularto element 25 described in FIG. 3. The table 30 has no intentionalspecific order or sorting along the column except for theabove-mentioned mapping of the rows. The first row comprises the valuesnum[0] and mv[0], whereby all rows comprise examples.

Table 33 is the result of the sorting element 25 and the binarizationelement 26 described in FIG. 3. The motion vectors 29 mv[i] are sortedby their respective number of occurrences 28 num[i], descending from thehighest number. Afterwards a new column is added to the table comprisingthe result of a comparison between the number num[i] and the filteringparameter 31, said parameter 31 being described in FIG. 3. If the num[i]is greater or equal to parameter 31, the additional entry in the row ofnum[i], a comparison index 39, is set to 1, otherwise it is set to 0. Asknown to a skilled person, other allocations and comparisons arepossible to mark specific motion vectors. The first row comprises thevalues num[0], mv[0] and the comparison index set as 1, whereby all rowscomprise examples.

Table 35 is the result of the substitution element 27 described in FIG.3. The motion vectors 29, which were marked with the additional entry inthe row (in this case with a zero in the comparison index 39), are nowsuccessively substituted by the motion vectors 29 mv[i] and theirrespective number of occurrences num[i] descending from the mostdominant motion vector 29 according to the number of occurrences 28num[i]. Moreover, every row is now indexed by an order index 40 inascending order until a predetermined number, said number depending onthe block size described later in FIG. 5. In FIGS. 4 and 5 the indicesrange from 0 to 15, but are not limited to this example. The first rowof table 35 comprises the values num[0], mv[0] and the order index setas 0, whereby all rows comprise examples.

FIG. 5 shows an example of an image frame 41 that shows the distributionof the indices 40 of the predominant motion vector list 35 over thecomplete image frame 41. The list 35 is the same as the list 16described in FIG. 2. Every index stands for a predictor motion vector oflist 35 described in FIG. 3 or 4. Since the motion estimation process isblock based, one entry in the list is used as predictor for theestimation process of one block 42. The picture 41 shows an example of16 predominant vectors, said vectors being indexed from 0 to 15. Thepattern is arranged in a way that the vector list 35 is distributed overthe predictor field in blocks 42 according to their index 40. Thisresults in a spatial equally distribution over the whole image frame 41,which is advantageous for the estimation process.

At the right border of the image frame 41 only half of the block 42 ofthe predominant vectors is inserted, since the frame 41 is not broadenough. Nevertheless every pixel 43 in the frame 41 is associated with apredominant predictor vector of list 35 according to the spatial equallydistribution to estimate the global motion.

1. A method for motion estimation of a first image frame and a secondimage frame, comprising: estimating a motion vector correlating aportion of pixels of the first and second image frame, the first andsecond image frame being part of an image frame sequence, wherein: themotion vector is obtained by a predominant motion detection generating aglobal motion vector based on a previously determined motion vector, thepreviously determined motion vector correlating a portion of pixels ofearlier image frames of the image frame sequence; the estimating themotion vector is performed on the basis of the global motion vector; andthe predominant motion detection comprises: forming a histogram on thebasis of the motion vector, filtering the motion vector, from among aplurality of motion vectors, on the basis of the histogram, thefiltering comprising: sorting the motion vectors; selecting, in abinarization, the motion vector from the sorted motion vectors; andsubstituting the motion vector, which is selected in the binarization,with a non-selected motion vector of the sorted motion vectors or apredetermined motion vector; and distributing the motion vector, whichis substituted as a result of the filtering, over at least a portion ofthe second image frame.
 2. A method according to claim 1, wherein: theforming the histogram, the filtering, and the distributing are performedin relation to at least one characteristic of the motion vector, thecharacteristics being represented by the histogram.
 3. A methodaccording to claim 1, wherein the motion vector describes a change ofpixel characteristics within two frames.
 4. A method according to claim1, wherein the distributing uses a predominant predictor fielddescribing a global motion of the at least a portion of the second imageframe.
 5. A method according to claim 1, wherein the forming a histogramcomprises: processing the motion vector into a histogram; and extractingcharacteristics out of the histogram.
 6. A method according to claim 1,wherein the binarization comprises selecting by a filtering parameter.7. A method according to claim 1, wherein the distributing distributes aplurality of filtered motion vectors spatial equally.
 8. A methodaccording to claim 1, further comprising: a segmentation detecting atleast one segment of an image frame, wherein the at least one segment ischaracterized by a specific distribution of motion vectors or pixelcharacteristics.
 9. A method according to claim 1, further comprising: amotion model classification detecting motion models of motion vectors.10. A method according to claim 9, wherein the motion models comprisetilting, panning, zooming, rotations, chaotic, and/or complex motions.11. A method according to claim 8, wherein the distributing distributesmotion vectors dependent on maximum occurrences of the motion vector oron a segment or on a calculation per position.
 12. A method forprocessing video signals into motion vector fields comprising: a motionestimation for receiving and processing input video signals andpredominant predictor fields, and outputting motion vector fields; apredominant motion detection comprising: forming a histogram on thebasis of a motion vector, filtering the motion vector, from among aplurality of motion vectors, on the basis of the histogram, thefiltering comprising: sorting the motion vectors, selecting, in abinarization, the motion vector from the sorted motion vectors, andsubstituting the motion vector, which is selected in the binarization,with a non-selected motion vector of the sorted motion vectors or apredetermined motion vector; and distributing the motion vector, whichis substituted as a result of the filtering, over at least a portion ofthe second image frame, wherein the method is operable to receive andprocess the motion vector fields, and output the predominant predictorfields.
 13. A method for processing video signals comprising: a videoprocessing receiving and processing input video signals and motionvector fields, and outputting video signals; and a method according toclaim 12 for receiving and processing input video signals, andoutputting motion vector fields.
 14. An apparatus to perform motionestimation of a first image frame and a second image frame, to estimatea motion vector correlating a portion of pixels of the first and secondimage frames, the first and second image frames being part of an imageframe sequence, the apparatus comprising: a predominant motion detectorconfigured to generate a global motion vector based on a previouslydetermined motion vector, the previously determined motion vectorcorrelating a portion of pixels of earlier image frames of the imageframe sequence; and a motion estimator configured to estimate the motionvector on the basis of the global motion vector, the predominant motiondetector comprising: a histogram device configured to form a histogramon the basis of the motion vector, a filtering device configured tofilter the motion vector, from among a plurality of motion vectors,based on the histogram, the filtering device comprising: a sortingdevice configured to sort the motion vectors, a binarization deviceconfigured to select the motion vector from the sorted motion vectors,and a substitution device operable to substitute the motion vector,which is selected by the binarization device, with a non-selected motionvector of the sorted motion vectors or a predetermined motion vector,and a distribution device configured to distribute the motion vector,which is substituted by the filtering device, over at least a portion ofthe second image frame.
 15. An apparatus according to claim 14, whereinthe histogram, filtering, and distribution devices are configured toperform respective functions in relation to at least one characteristicof the motion vector, the characteristic being represented by thehistogram.
 16. An apparatus according to claim 14, wherein the motionvector describes a change of pixel characteristics between two frames.17. An apparatus according to claim 14, wherein the distribution deviceis configured to use a predominant predictor field describing a globalmotion of the at least a portion of the second image frame.
 18. Anapparatus according to claim 14, wherein the histogram device comprises:a histogram calculation device configured to process the motion vectorinto a histogram; and a histogram analysis device configured to extractcharacteristics out of the histogram.
 19. An apparatus according toclaim 14, wherein the binarization device is configured to select by afiltering parameter.
 20. An apparatus according to claim 14, wherein thedistribution device is configured to distribute a plurality of filteredmotion vectors spatial equally.
 21. An apparatus according to claim 14,further comprising: a segmentation device configured to detect at leastone segment of an image frame, wherein the at least one segment ischaracterized by a specific distribution of motion vectors or pixelcharacteristics.
 22. An apparatus according to claim 14, furthercomprising: a motion model classification device configured to detectmotion models of motion vectors.
 23. An apparatus according to claim 22,wherein the motion models comprise tilting, panning, zooming, rotations,chaotic, and/or complex motions.
 24. An apparatus according to claim 21,wherein the distribution device is configured to distribute motionvectors dependent on maximum occurrences of the motion vector or on asegment or on a calculation per position.
 25. An apparatus operable toprocess video signals into motion vector fields comprising: a motionestimation device configured to receive and process input video signalsand predominant predictor fields, and output motion vector fields; and apredominant motion detection device configured to generate a globalmotion vector based on a previously determined motion vector, thepreviously determined motion vector correlating a portion of pixels ofearlier image frames of the image frame sequence, the predominant motiondetector comprising: a histogram device configured to form a histogramon the basis of the motion vector; a filtering device configured tofilter the motion vector on, from among a plurality of motion vectors,based on the histogram, the filtering device comprising: a sortingdevice configured to sort the motion vectors, a binarization deviceconfigured to select the motion vector from the sorted motion vectors,and a substitution device configured to substitute the motion vector,which is selected by the binarization device, with a non-selected motionvector of the sorted motion vectors or a predetermined motion vector;and a distribution device configured to distribute the motion vector,which is substituted by the filtering device, over at least a portion ofthe second image frame, wherein the apparatus is operable to receive andprocess the motion vector fields, and output the predominant predictorfields.
 26. A system to process video signals comprising: a videoprocessing device to receive and process input video signals and motionvector fields, and output video signals; and an apparatus according toclaim 25, wherein the apparatus is configured to receive and processinput video signals, and output motion vector fields.